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advanced_sentiments_build/
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name_gender_build/
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build_name_gender:
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python3 -m coffeehouse_dltc --train-model name_gender
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build_email_contents:
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python3 -m coffeehouse_dltc --train-model advanced_sentiments
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build:
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make build_name_gender
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make build_email_contents
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test_name_gender:
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python3 -m coffeehouse_dltc --test-model name_gender_build
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test_advanced_sentiments:
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python3 -m coffeehouse_dltc --test-model advanced_sentiments_build
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clean:
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@for f in $(shell ls name_gender); do \
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echo "Processing $${f}" && sort -u "name_gender/$${f}" > "name_gender/$${f}.clean" && rm "name_gender/$${f}" && mv "name_gender/$${f}.clean" "name_gender/$${f}"; \
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done
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@for f in $(shell ls advanced_sentiments); do \
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echo "Processing $${f}" && sort -u "advanced_sentiments/$${f}" > "advanced_sentiments/$${f}.clean" && rm "advanced_sentiments/$${f}" && mv "advanced_sentiments/$${f}.clean" "advanced_sentiments/$${f}"; \
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done
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# ProfileIO
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This is the source code for ProfileIO models which are intended to be used to
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identify specifhic characters about individuals.
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## Fetch model
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CoffeeHousePy comes with tools to fetch the latest models available, add the
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following code to your project to fetch the model
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```python
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from resource_fetch import ResourceFetch
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rf = ResourceFetch()
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spam_classification = rf.fetch("Intellivoid", "CoffeeHouseData-ProfileIO")
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```
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## Building The Model
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Builiding the model requires CoffeeHousePy to be installed on the system,
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you can build the model in the foreground by running one of these commands
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```sh
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# Build the model (Full)
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make build
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# Build advanced_sentiments
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make build_advaned_sentiments
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# Build name_gender
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make build_name_gender
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```
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## Testing the model
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You can run an interactive Commandline Interface test to predict
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the output of the model's predictions by running the DLTC tester.
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```sh
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make test_advanced_sentiments
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make test_name_gender
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```
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## Cleaning the data
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Once the data is generated, it's reccomended to clean the and
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organize the new data that was generated.
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```sh
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make clean
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```
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{
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"model": {
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"name": "Advanced Sentiments",
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"model_name": "advanced_sentiments",
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"author": "Zi Xing",
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"version": "1.0.0.0",
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"description": "Model for predicting multiple sentminal emotions towards user input"
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},
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"training_properties":{
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"epoch": 15,
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"vec_dim": 100,
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"test_ratio": 0.2,
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"architecture": "cnn",
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"batch_size": 64
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},
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"classification": [
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{"l": "anger", "f": "anger.dat"},
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{"l": "neutral", "f": "neutral.dat"},
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{"l": "happiness", "f": "happiness.dat"},
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{"l": "affection", "f": "affection.dat"},
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{"l": "sadness", "f": "sadness.dat"}
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]
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}
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{
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"model": {
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"name": "Name Gender Prediction",
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"model_name": "name_gender",
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"author": "Zi Xing",
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"version": "1.0.0.0",
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"description": "Model for predicting the the gender based off a name"
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},
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"training_properties":{
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"epoch": 30,
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"vec_dim": 100,
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"test_ratio": 0.2,
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"architecture": "cnn",
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"batch_size": 64
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},
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"classification": [
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{"l": "male", "f": "male_names.dat"},
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{"l": "female", "f": "female_names.dat"}
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]
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}
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